CAREER: Understanding metal/support interactions in catalysis with statistical learning

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Heterogeneous catalysts consisting of metal nanoparticles supported on oxide surfaces are essential for promoting rapid and energy efficient manufacturing of fuels and chemicals, as well as controlling emissions of environmental pollutants. Catalyst research and development has historically relied primarily on experimental methods – often involving time- and resource-intensive materials screening. In recent years, however, increasing effort has been directed toward predictive design of advanced catalyst technologies, enabled by advances in theory, computational methods, and artificial intelligence. The project develops and applies advanced simulation tools, based on machine learning, which will be used to establish strategies for controlling metal/support interactions to enhance catalyst stability, activity, and selectivity. Models built with these tools will accelerate research efforts to design new types of catalysts consisting of either isolated metal atoms or small clusters dispersed on high-surface area metal oxide supports. In addition to improving catalyst reactivity, product selectivity, and stability, the project will enable discovery and design of catalysts that reduce the amount of expensive and strategic metals (such as platinum, palladium, and rhodium) used widely in chemical manufacturing and pollution control. Beyond improving catalytic technologies, the project will extend educational efforts by establishing an outreach program for underserved communities through the Tapia Center for Excellence and Equity in Education at Rice University. Synergistic interactions between metal nanoparticles and oxide supports are known to influence catalyst performance. The goal of the project is to develop a theoretical framework that uses statistical-based machine learning, together with density functional theory, to understand metal/support interactions (MSIs) in heterogeneous catalysis. The first objective is to apply computationally efficient statistical learning methodologies to identify the physical descriptors of metal binding on oxide supports, which in turn can be used to predict metal sintering rates and cluster morphology. The second objective is to predict catalytic activity and selectivity by constructing models that relate MSIs to adsorbate binding energies and the associated kinetic barriers that control mechanisms for prototypical model reactions, such as carbon monoxide oxidation. The third objective is to apply thermodynamic analyses to understand the stability of support modifications that are strategically introduced to enhance MSIs. Thus, the overall methodology not only will identify desirable surface modifications for controlling catalytic behavior by tuning MSIs, but also will predict the environments in which such modifications can be achieved most readily.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.